期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 14, 期 4, 页码 928-946出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186005X77685
关键词
Mabalanobis squared distance; minimum covariance determinant; outlier detection; robust estimation
Mahalanobis-type distances in which the shape matrix is derived from a consistent, high-breakdown robust multivariate location and scale estimator have an asymptotic chi-squared distribution as is the case with those derived from the ordinary covariance matrix. For example, Rousseeuw's minimum covariance determinant (MCD) is a robust estimator with a high breakdown. However, even in quite large samples, the chi-squared approximation to the distances of the sample data from the MCD center with respect to the MCD shape is poor. We provide an improved F approximation that gives accurate outlier rejection points for various sample sizes.
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